There are many instances where a sequence of occurrences becomes highly significant when placed against a timeline. Examples of these occur in control environments such as manufacturing, military I&W, environmental control, and a myriad of other process control mechanisms. Classification of these time-based patterns using connectionist models is a flexible means of capturing and assessing such information. This investigation presents an application of connectionist models to classify time-based indications and warning (I&W) patterns. Three threat categories, with increasing degrees of activity overlap within each category, were selected as a problem set. A simulation was developed which generated realistic I&W messages for activities within each category. A recurrent network was then trained to estimate, based upon these simulated I&W messages, which of three possible states an aggressor nation might be in. Each network has an input unit for each of its activity indicators, some number of hidden units, and three outputs (one for each of the three possible states). Training was conducted using epochwise backpropagation through time.